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Multitrophic arthropod diversity mediates tree diversity effects on primary productivity

Nature Ecology & Evolution volume 7, pages 832–840 (2023)Cite this article

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Abstract

Forests sustain 80% of terrestrial biodiversity and provide essential ecosystem services. Biodiversity experiments have demonstrated that plant diversity correlates with both primary productivity and higher trophic diversity. However, whether higher trophic diversity can mediate the effects of plant diversity on productivity remains unclear. Here, using 5 years of data on aboveground herbivorous, predatory and parasitoid arthropods along with tree growth data within a large-scale forest biodiversity experiment in southeast China, we provide evidence of multidirectional enhancement among the diversity of trees and higher trophic groups and tree productivity. We show that the effects of experimentally increased tree species richness were consistently positive for species richness and abundance of herbivores, predators and parasitoids. Richness effects decreased as trophic levels increased for species richness and abundance of all trophic groups. Multitrophic species richness and abundance of arthropods were important mediators of plant diversity effects on tree productivity, suggesting that optimizing forest management for increased carbon capture can be more effective when the diversity of higher trophic groups is promoted in concert with that of trees.

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Fig. 1: Graphical illustration of the research framework and hypotheses.
Fig. 2: Relationships between tree species richness and species richness and abundance of higher trophic groups.
Fig. 3: Relationships between species richness and abundance of higher trophic groups and primary productivity.
Fig. 4: SEMs of tree species richness, functional traits (tree FD and CWM), year and overall arthropod or herbivore and enemy species richness or abundance explaining tree productivity.

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Data availability

The plot-level arthropod diversity, primary productivity and FD data are available at https://data.botanik.uni-halle.de/bef-china/datasets/659.

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Acknowledgements

We thank J. Chen, S. Guo and several local assistants for their help in the field sampling. We thank Y. Liang for discussion about the statistics. We also thank C. Scherber for providing data from the Jena biodiversity experiment. This study was supported by the National Key Research Development Program of China (2022YFF0802300), the National Natural Science Foundation of China (32222055) and the Strategic Priority Research Program of the Chinese Academy of Sciences (XDB31000000). X.L. was funded by the Youth Innovation Promotion Association CAS (2019082). B.S. was supported by the University Research Priority Program Global Change and Biodiversity of the University of Zurich. C.Z. and his laboratory were supported by the National Science Foundation of China for Distinguished Young Scholars (31625024). Y.L., A.S., P.A., H.B., K.M. and X.L. acknowledge the International Research Training Group TreeDì jointly funded by the Deutsche Forschungsgemeinschaft (German Research Foundation)—319936945/GRK2324 and the University of Chinese Academy of Science.

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Authors and Affiliations

  1. State Key Laboratory of Vegetation and Environmental Change, Institute of Botany, Chinese Academy of Sciences, Beijing, China

    Yi Li, Shan Li, Keping Ma & Xiaojuan Liu

  2. Department of Geography, Remote Sensing Laboratories, University of Zurich, Zurich, Switzerland

    Bernhard Schmid

  3. Forest Nature Conservation, University of Göttingen, Göttingen, Germany

    Andreas Schuldt

  4. Key Laboratory of Zoological Systematics and Evolution, Institute of Zoology, Chinese Academy of Sciences, Beijing, China

    Ming-Qiang Wang, Douglas Chesters & Chao-Dong Zhu

  5. CAS Key Laboratory of Mountain Ecological Restoration and Bioresource Utilization and Ecological Restoration and Biodiversity Conservation Key Laboratory of Sichuan Province, Chengdu Institute of Biology, Chinese Academy of Sciences, Chengdu, China

    Ming-Qiang Wang

  6. Chair of Nature Conservation and Landscape Ecology, Faculty of Environment and Natural Resources, University of Freiburg, Freiburg, Germany

    Felix Fornoff

  7. Ecological Networks, Technical University of Darmstadt, Darmstadt, Germany

    Michael Staab

  8. College of Pharmacy, Guizhou University of Traditional Chinese Medicine, Guiyang, China

    Peng-Fei Guo

  9. Institute of Biology/Geobotany and Botanical Garden, Martin Luther University Halle-Wittenberg, Halle, Germany

    Perttu Anttonen & Helge Bruelheide

  10. German Centre for Integrative Biodiversity Research (iDiv) Halle-Jena-Leipzig, Leipzig, Germany

    Perttu Anttonen & Helge Bruelheide

  11. College of Life Sciences, University of Chinese Academy of Sciences, Beijing, China

    Chao-Dong Zhu & Keping Ma

  12. Zhejiang Qianjiangyuan Forest Biodiversity National Observation and Research Station, Institute of Botany, Chinese Academy of Sciences, Beijing, China

    Keping Ma & Xiaojuan Liu

  13. China National Botanical Garden, Beijing, China

    Keping Ma & Xiaojuan Liu

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  1. Yi Li
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Contributions

X.L. conceived the study. X.L., K.M., S.L., Y.L., A.S., M.-Q.W., F.F., M.S., P.-F.G., P.A. and C.-D.Z. were responsible for data collection. Y.L. and X.L. performed statistical analyses with contributions from B.S., A.S. and M.S. The initial paper was prepared by Y.L. and X.L. with contributions from B.S., A.S., M.S., D.C., H.B. and K.M. All co-authors helped improve the paper.

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Correspondence to Keping Ma or Xiaojuan Liu.

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Extended data

Extended Data Fig. 1 Hypotheses framework for relationships among tree species richness, functional traits (indicated by tree functional diversity, FD; and community-weighted mean, CWM), arthropod (indicated by their species richness and abundance), and tree productivity.

Model (a) was constructed based on averaged overall arthropod data. Model (b) was constructed based on 5-year trophic-resolved (that is, herbivores and natural enemies are partitioned) data, in which we included ‘year’ as a linear predictor. The framework is based on theoretical expectations and correlations among multiple variables. Grey arrows indicate hypothesized causal relationships.

Extended Data Fig. 2 Predation rate of model caterpillars by arthropods and birds.

Predation rate of arthropods and birds on model caterpillars in spring (a, n = 383) and summer (b, n = 375); relationships between predation rate of arthropods on model caterpillars and tree species richness in spring (c) and summer (d). Grey bars indicate the averaged predation rate on model caterpillars by arthropods and birds, and black error bars represent the standard deviation. Solid lines show significant (P < 0.05) effects. The grey-shaded zone covers the 95% confidence interval. All tests were two-sided. The x-axes are on a log2-scale for tree species richness.

Extended Data Fig. 3 Structural equation model testing if tree species richness and functional traits affect arthropod species richness and abundance through tree productivity.

The model fit for both models (model a: Fisher’s C = 4.09, P = 0.394, DF = 4, AIC = 26.090; model b: Fisher’s C = 0.299, P = 0.861, DF = 2, AIC = 24.299) suggested that tree-diversity effects on arthropod species richness (a) or abundance (b) are not mediated through tree productivity. Green lines show significant (P < 0.05) positive relationships, and brown lines show significant negative relationships, while grey lines show marginally significant relationships (0.05 ≤ P < 0.1). Standardized path coefficients are shown in each path with asterisks indicating significance (* P < 0.05, ** P < 0.01, and *** P < 0.001). Percentage values (conditional R2) are shown below the corresponding variables. Arrow widths are scaled by the absolute values of the standardized path coefficients.

Extended Data Fig. 4 Structural equation model testing how plant species richness, arthropod species richness or abundance explaining plant biomass, using the data collected from the Jena biodiversity experiment.

Model (a) (Fisher’s C = 5.249, P = 0.072, DF = 2, AIC = 21.249) and (b) (Fisher’s C = 8.247, P = 0.016, DF = 2, AIC = 24.247) were constructed based on overall arthropod species richness and abundance. Model (c) and (d) were constructed based on trophic-resolved (herbivores and enemies partitioned) data (c: Fisher’s C = 3.876, P = 0.144, DF = 2, AIC = 31.876; d: Fisher’s C = 3.042, P = 0.219, DF = 2, AIC = 31.042). Green lines show significant (P < 0.05) positive relationships, and grey lines show marginally significant paths (0.05 ≤ P < 0.1). Standardized path coefficients are shown next to each path with asterisks indicating significance (* P < 0.05, ** P < 0.01, and *** P < 0.001). Percentage values (conditional R2) are shown below the corresponding variables.

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Li, Y., Schmid, B., Schuldt, A. et al. Multitrophic arthropod diversity mediates tree diversity effects on primary productivity. Nat Ecol Evol 7, 832–840 (2023). https://doi.org/10.1038/s41559-023-02049-1

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